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Background: Spinal muscular atrophy (SMA) is a neuromuscular disease caused by homozygous deletion or loss-of-function mutations of the survival of motor neuron 1 (SMN1) gene, resulting in reduced levels of SMN protein throughout the body. Patients with SMA may have multiple tissue defects, which could present prior to neuromuscular symptoms. Objective: To assess the signs, comorbidities and potential extraneural manifestations associated with SMA in treatment-naïve patients. Methods: This observational, retrospective and matched-cohort study used secondary insurance claims data from the US IBM® MarketScan® Commercial, Medicaid and Medicare Supplemental databases between 01/01/2000 and 12/31/2013. Treatment-naïve individuals aged≤65 years with≥2 International Classification of Diseases, Ninth Revision (ICD-9) SMA codes were stratified into four groups (A–D), according to age at index (date of first SMA code recorded) and type of ICD-9 code used, and matched with non-SMA controls. The occurrence of ICD-9 codes, which were converted to various classifications (phecodes and system classes), were compared between groups in pre- and post-index periods. Results: A total of 1,457 individuals with SMA were included and matched to 13,362 controls. Increasing numbers of SMA-associated phecodes and system classes were generally observed from pre- to post-index across all groups. The strongest associations were observed in the post-index period for the youngest age groups. Endocrine/metabolic disorders were associated with SMA in almost all groups and across time periods. Conclusions: This exploratory study confirmed the considerable disease burden in patients with SMA and identified 305 unique phecodes associated with SMA, providing a rationale for further research into the natural history and progression of SMA, including extraneural manifestations of the disease.

One of the primary tools that researchers use to predict risk is the case-control study. We identify a flaw, temporal bias, that is specific to and uniquely associated with these studies that occurs when the study period is not representative of the data that clinicians have during the diagnostic process. Temporal bias acts to undermine the validity of predictions by over-emphasizing features close to the outcome of interest. We examine the impact of temporal bias across the medical literature, and highlight examples of exaggerated effect sizes, false-negative predictions, and replication failure. Given the ubiquity and practical advantages of case-control studies, we discuss strategies for estimating the influence of and preventing temporal bias where it exists., One of the primary tools that researchers use to predict risk is the case-control study. Here, the authors identify a flaw, temporal bias, that is specific to and uniquely associated with these studies that occurs when the study period is not representative of the data that clinicians have during the diagnostic process, undermining the validity of predictions.

The mammalian brain is complex, with multiple cell types performing a variety of diverse functions, but exactly how each cell type is affected in aging remains largely unknown. Here we performed a single-cell transcriptomic analysis of young and old mouse brains. We provide comprehensive datasets of aging-related genes, pathways and ligand-receptor interactions in nearly all brain cell types. Our analysis identified gene signatures that vary in a coordinated manner across cell types and gene sets that are regulated in a cell-type specific manner, even at times in opposite directions. These data reveal that aging, rather than inducing a universal program, drives a distinct transcriptional course in each cell population, and they highlight key molecular processes, including ribosome biogenesis, underlying brain aging. Overall, these large-scale datasets (accessible online at https://portals.broadinstitute.org/single_cell/study/aging-mouse-brain ) provide a resource for the neuroscience community that will facilitate additional discoveries directed towards understanding and modifying the aging process.

The etiological underpinnings of many CNS disorders are not well understood. This is likely due to the fact that individual diseases aggregate numerous pathological subtypes, each associated with a complex landscape of genetic risk factors. To overcome these challenges, researchers are integrating novel data types from numerous patients, including imaging studies capturing broadly applicable features from patient-derived materials. These datasets, when combined with machine learning, potentially hold the power to elucidate the subtle patterns that stratify patients by shared pathology. In this study, we interrogated whether high-content imaging of primary skin fibroblasts, using the Cell Painting method, could reveal disease-relevant information among patients. First, we showed that technical features such as batch/plate type, plate, and location within a plate lead to detectable nuisance signals, as revealed by a pre-trained deep neural network and analysis with deep image embeddings. Using a plate design and image acquisition strategy that accounts for these variables, we performed a pilot study with 12 healthy controls and 12 subjects affected by the severe genetic neurological disorder spinal muscular atrophy (SMA), and evaluated whether a convolutional neural network (CNN) generated using a subset of the cells could distinguish disease states on cells from the remaining unseen control-SMA pair. Our results indicate that these two populations could effectively be differentiated from one another and that model selectivity is insensitive to batch/plate type. One caveat is that the samples were also largely separated by source. These findings lay a foundation for how to conduct future studies exploring diseases with more complex genetic contributions and unknown subtypes.

Background Characterization of prediagnostic Parkinson’s Disease (PD) and early prediction of subsequent development are critical for preventive interventions, risk stratification and understanding of disease pathology. This study aims to characterize the role of the prediagnostic period in PD and, using selected features from this period as novel interception points, construct a prediction model to accelerate the diagnosis in a real-world setting. Methods We constructed two sets of machine learning models: a retrospective approach highlighting exposures up to 5 years prior to PD diagnosis, and an alternative model that prospectively predicted future PD diagnosis from all individuals at their first diagnosis of a gait or tremor disorder, these being features that appeared to represent the initiation of a differential diagnostic window. Results We found many novel features captured by the retrospective models; however, the high accuracy was primarily driven from surrogate diagnoses for PD, such as gait and tremor disorders, suggesting the presence of a distinctive differential diagnostic period when the clinician already suspected PD. The model utilizing a gait/tremor diagnosis as the interception point, achieved a validation AUC of 0.874 with potential time compression to a future PD diagnosis of more than 300 days. Comparisons of predictive diagnoses between the prospective and prediagnostic cohorts suggest the presence of distinctive trajectories of PD progression based on comorbidity profiles. Conclusions Overall, our machine learning approach allows for both guiding clinical decisions such as the initiation of neuroprotective interventions and importantly, the possibility of earlier diagnosis for clinical trials for disease modifying therapies.

Modeling the relationship between chemical structure and molecular activity is a key goal in drug development. Many benchmark tasks have been proposed for molecular property prediction, but these tasks are generally aimed at specific, isolated biomedical properties. In this work, we propose a new cross-modal small molecule retrieval task, designed to force a model to learn to associate the structure of a small molecule with the transcriptional change it induces. We develop this task formally as multi-view alignment problem, and present a coordinated deep learning approach that jointly optimizes representations of both chemical structure and perturbational gene expression profiles. We benchmark our results against oracle models and principled baselines, and find that cell line variability markedly influences performance in this domain. Our work establishes the feasibility of this new task, elucidates the limitations of current data and systems, and may serve to catalyze future research in small molecule representation learning., Accepted for oral presentation at the Pacific Symposium of Biocomputing, 2021

ObjectiveWe investigated the presence of non-neuromuscular phenotypes in patients affected by Spinal Muscular Atrophy (SMA), a disorder caused by a mutation in the Survival of Motor Neuron (SMN) gene, and whether these phenotypes may be clinically detectable prior to clinical signs of neuromuscular degeneration and therefore independent of muscle weakness.MethodsWe utilized a de-identified database of insurance claims to explore the health of 1,038 SMA patients compared to controls. Two analyses were performed: (1) claims from the entire insurance coverage window; and (2) for SMA patients, claims prior to diagnosis of any neuromuscular disease or evidence of major neuromuscular degeneration to increase the chance that phenotypes could be attributed directly to reduced SMN levels. Logistic regression was used to determine whether phenotypes were diagnosed at significantly different rates between SMA patients and controls and to obtain covariate-adjusted odds ratios.ResultsResults from the entire coverage window revealed a broad spectrum of phenotypes that are differentially diagnosed in SMA subjects compared to controls. Moreover, data from SMA patients prior to their first clinical signs of neuromuscular degeneration revealed numerous non-neuromuscular phenotypes including defects within the cardiovascular, gastrointestinal, metabolic, reproductive, and skeletal systems. Furthermore, our data provide evidence of a potential ordering of disease progression beginning with these non-neuromuscular phenotypes.ConclusionsOur data point to a direct relationship between early, detectable non-neuromuscular symptoms and SMN deficiency. Our findings are particularly important for evaluating the efficacy of SMN-increasing therapies for SMA, comparing the effectiveness of local versus systemically delivered therapeutics, and determining the optimal therapeutic treatment window prior to irreversible neuromuscular damage.

The mammalian brain is complex, with multiple cell types performing a variety of diverse functions, but exactly how the brain is affected with aging remains largely unknown. Here we performed a single-cell transcriptomic analysis of young and old mouse brains. We provide a comprehensive dataset of aging-related genes, pathways and ligand-receptor interactions in nearly all brain cell types. Our analysis identified gene signatures that vary in a coordinated manner across cell types and gene sets that are regulated in a cell type specific manner, even at times in opposite directions. Thus, our data reveals that aging, rather than inducing a universal program drives a distinct transcriptional course in each cell population. These data provide an important resource for the aging community and highlight key molecular processes, including ribosomal biogenesis, underlying aging. We believe that this large-scale dataset, which is publicly accessible online (aging-mouse-brain), will facilitate additional discoveries directed towards understanding and modifying the aging process.

The genetic complexity, clinical variability, and inaccessibility of affected tissue in neurodegenerative and neuropsychiatric disorders have largely prevented the development of effective disease-modifying therapeutics. A precision medicine approach that integrates genomics, deep clinical phenotyping, and patient stem cell models may facilitate identification of underlying biological drivers and targeted drug development.

The next frontier in medicine involves better quantifying human traits, known as “phenotypes.” Biological markers have been directly associated with disease risks, but poor measurement of behaviors such as diet and exercise limits our understanding of preventive measures. By joining together an uncommonly wide range of disciplines and expertise, the Kavli HUMAN Project will advance measurement of behavioral phenotypes, as well as environmental factors that impact behavior. By following the same individuals over time, KHP will liberate new understanding of dynamic links between behavioral phenotypes, disease, and the broader environment. As KHP advances understanding of the bio-behavioral complex, it will seed new approaches to the diagnosis, prevention, and treatment of human disease.

SummaryParkinson’s disease (PD) has been attributed to a combination of genetic and nongenetic factors. We studied a set of monozygotic twins harboring the heterozygous glucocerebrosidase mutation (GBA N370S) but clinically discordant for PD. We applied induced pluripotent stem cell (iPSC) technology for PD disease modeling using the twins’ fibroblasts to evaluate and dissect the genetic and nongenetic contributions. Utilizing fluorescence-activated cell sorting, we obtained a homogenous population of “footprint-free” iPSC-derived midbrain dopaminergic (mDA) neurons. The mDA neurons from both twins had ∼50% GBA enzymatic activity, ∼3-fold elevated α-synuclein protein levels, and a reduced capacity to synthesize and release dopamine. Interestingly, the affected twin’s neurons showed an even lower dopamine level, increased monoamine oxidase B (MAO-B) expression, and impaired intrinsic network activity. Overexpression of wild-type GBA and treatment with MAO-B inhibitors normalized α-synuclein and dopamine levels, suggesting a combination therapy for the affected twin.

Dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and proton (1H) magnetic resonance spectroscopy (MRS) provide structural and biochemical information, including vascular volume, vascular permeability and tissue metabolism. In this study, we performed analysis of the enhancement characteristic from DCE-MRI and the biochemical information provided by two-dimensional (2D) Localized Correlated Spectroscopy (L-COSY) MRS to determine the sensitivity and specificity of using DCE-MRI alone compared to the combination with 2D MRS. The metabolite ratios from the 2D MRS spectra were analyzed using multivariate statistical analyses to determine a method capable of automatic separation of the patient cohort into malignant and benign lesions. A total of 24 lesions were studied with 21 diagnosed accurately using the enhancement characteristics alone resulting in sensitivity and specificity of 100% and 73%, respectively. Analysis of the 2D MRS data demonstrated a significant difference (p

Although amyotrophic lateral sclerosis (ALS), also referred as ‘Lou Gehrig's Disease,’ was first described in 1869 and the first disease-associated gene was discovered almost 20 years ago, the disease etiology is still not fully understood and treatment options are limited to one drug approved by the US Food and Drug Administration (FDA). The slow translational progress suggests that current research models are not ideal to study such a complicated disease and need to be re-examined. Progress will require greater insight into human genes and biology involved in ALS susceptibility, as well as a deeper understanding of disease phenotype at the histological and molecular levels. Improving human disease outcome will require directing focus toward improved assessment technologies and innovative approaches.

Human peripheral blood and umbilical cord blood represent attractive sources of cells for reprogramming to induced pluripotent stem cells (iPSCs). However, to date, most of the blood-derived iPSCs were generated using either integrating methods or starting from T-lymphocytes that have genomic rearrangements thus bearing uncertain consequences when using iPSC-derived lineages for disease modeling and cell therapies. Recently, both peripheral blood and cord blood cells have been reprogrammed into transgene-free iPSC using the Sendai viral vector. Here we demonstrate that peripheral blood can be utilized for medium-throughput iPSC production without the need to maintain cell culture prior to reprogramming induction. Cell reprogramming can also be accomplished with as little as 3000 previously cryopreserved cord blood cells under feeder-free and chemically defined Xeno-free conditions that are compliant with standard Good Manufacturing Practice (GMP) regulations. The first iPSC colonies appear 2–3 weeks faster in comparison to previous reports. Notably, these peripheral blood- and cord blood-derived iPSCs are free of detectable immunoglobulin heavy chain (IGH) and T cell receptor (TCR) gene rearrangements, suggesting they did not originate from B- or T- lymphoid cells. The iPSCs are pluripotent as evaluated by the scorecard assay and in vitro multi lineage functional cell differentiation. Our data show that small volumes of cryopreserved peripheral blood or cord blood cells can be reprogrammed efficiently at a convenient, cost effective and scalable way. In summary, our method expands the reprogramming potential of limited or archived samples either stored at blood banks or obtained from pediatric populations that cannot easily provide large quantities of peripheral blood or a skin biopsy. Electronic supplementary material The online version of this article (doi:10.1007/s12015-015-9586-8) contains supplementary material, which is available to authorized users.

Induced pluripotent stem cells (iPSCs) have elicited excitement in both the scientific and ethics communities for their potential to advance basic and translational research. They have been hailed as an alternative to derivation from embryos that provides a virtually unlimited source of pluripotent stem cells for research and therapeutic applications. However, research with iPSCs is ethically complex, uniquely encompassing the concerns associated with genomics, immortalized cell lines, transplantation, human reproduction, and biobanking. Prospective donation of tissue specimens for iPSC research thus requires an approach to informed consent that is constructed for this context. Even in the nascent stages of this field, approaches to informed consent have been variable in ways that threaten the simultaneous goals of protecting donors and safeguarding future research and translation, and investigators are seeking guidance. We address this need by providing concrete recommendations for informed consent that balance the perspectives of a variety of stakeholders. Our work combines analysis of consent form language collected from investigators worldwide with a conceptual balancing of normative ethical concerns, policy precedents, and scientific realities. Our framework asks people to consent prospectively to a broad umbrella of foreseeable research, including future therapeutic applications, with recontact possible in limited circumstances. We argue that the long-term goals of regenerative medicine, interest in sharing iPSC lines, and uncertain landscape of future research all would be served by a framework of ongoing communication with donors. Our approach balances the goals of iPSC and regenerative medicine researchers with the interests of individual research participants.

Exploiting the speed benefits of echo-planar imaging (EPI), the echo-planar spectroscopic imaging (EPSI) sequence facilitates recording of one spectral and two to three spatial dimensions faster than the conventional magnetic resonance spectroscopic imaging (MRSI). A novel four dimensional (4D) echo-planar correlated spectroscopic imaging (EP-COSI) was implemented on a whole body 3 T MRI scanner combining two spectral with two spatial encodings. Similar to EPSI, the EP-COSI sequence used a bipolar spatial read-out train facilitating simultaneous spatial and spectral encoding, and the conventional phase and spectral encodings for the other spatial and indirect spectral dimensions, respectively. Multiple 2D correlated spectroscopy (COSY) spectra were recorded over the spatially resolved volume of interest (VOI) localized by a train of three slice-selective radiofrequency (RF) pulses (90°-180°-90°). After the initial optimization using phantom solutions, the EP-COSI data were recorded from the lower leg of eight healthy volunteers including one endurance trained volunteer. Pilot results showed acceptable spatial and spectral quality achievable using the EP-COSI sequence. There was a detectable separation of cross peaks arising from the skeletal muscle intramyocellular lipids (IMCLs) and extramyocellular lipids (EMCLs) saturated and unsaturated pools. Residual dipolar interaction between the N-methylene and N-methyl protons of creatine/phosphocreatine (Cr/PCr) was also observed in the tibialis anterior region.

Proton (1H) MRS enables non-invasive biochemical assay with the potential to characterize malignant, benign and healthy breast tissues. In vitro studies using perchloric acid extracts and ex vivo magic angle spinning spectroscopy of intact biopsy tissues have been used to identify detectable metabolic alterations in breast cancer. The challenges of 1H MRS in vivo include low sensitivity and significant overlap of resonances due to limited chemical shift dispersion and significant inhomogeneous broadening at most clinical magnetic field strengths. Improvement in spectral resolution can be achieved in vivo and in vitro by recording the MR spectra spread over more than one dimension, thus facilitating unambiguous assignment of metabolite and lipid resonances in breast cancer. This article reviews the recent progress with two-dimensional MRS of breast cancer in vitro, ex vivo and in vivo. The discussion includes unambiguous detection of saturated and unsaturated fatty acids, as well as choline-containing groups such as free choline, phosphocholine, glycerophosphocholine and ethanolamines using two-dimensional MRS. In addition, characterization of invasive ductal carcinomas and healthy fatty/glandular breast tissues non-invasively using the classification and regression tree (CART) analysis of two-dimensional MRS data is reviewed. Copyright © 2008 John Wiley & Sons, Ltd.

Irradiation perturbs the homeostatic network linking parenchymal, mesenchymal, and vascular cells within tissues. Normal communication between cells through soluble, matrix, and cell-associated ligands and receptors is altered so as to set in motion a seemingly inexorable series of events aimed at tissue regeneration and healing. In late responding normal tissues where cell death is not compensated for by rapid regeneration, this process unfortunately often culminates in symptomatic complications of radiation exposure. Cytokines and their receptors are prominent in driving the cascade of molecular responses using the balance between seemingly mutually antagonistic molecules to control and direct the healing processes. There is strong evidence from preclinical models for the importance of cytokine-driven pathways in late radiation damage and growing evidence in humans for their relevance to radiation-induced disease. This review aims to show some general aspects of the molecular torrents that drive responses in irradiated tissues before and during the development of late effects. It attempts to collate some of the findings from preclinical models of late lung, central nervous system, skin, and intestinal damage and from clinical studies in the belief that understanding how irradiation perturbs the cellular communication networks will allow rationale intervention for mitigating late radiation tissue damage and carcinogenesis.

Purpose:Magnetic Resonance Spectroscopy (MRS) allows noninvasive measurements of the concentrations of different metabolites in human breast in vivo. Two‐dimensional correlated spectroscopy (2D COSY) of breast tissues showed that in addition to Choline, several other metabolite ratios are different between malignant tumor and healthy tissues. The purpose of this study is to investigate the possibility of using Choline/Noise ratio (CNR) and other metabolite ratios from 2D COSY of breast tissues to detect breast cancer.Method and Materials: A total of 31 women participated in this study, including 13 healthy women, 9 subjects with malignant tumors, 9 women with benign tumors. Each 2D spectrum was acquired from malignant tumor, benign tumor, healthy fatty tissue or healthy glandular tissue. The size of each volume of interest was 1×1×1cm3, the total scan time was 12 minutes. All 2D MRS data files were processed using Felix2000 software package. Each 2D spectrum contains : Water (4.8, 4.8)ppm, Fat (1.4, 1.4)ppm ; Methyl Fat (0.9, 0.9) ppm ; Olefinic Fat (5.4, 5.4)ppm; Choline (3.3, 3.3)ppm; UFR(2.1, 5.4)ppm ; UFL (2.9, 5.4) ppm; TGFR (4.3, 5.3)ppm. CNR, 9 diagonal and 9 cross peak metabolite ratios were calculated for each spectrum. CNR and two statistical analysis methods (Fisher's linear discriminate, Classification and regression tree) were used to distinguish between malignant tumors from other breast tissues.Results: The CNR and 9 metabolite ratios are significantly different (p